GIS AND REMOTE SENSING FOR WATER RESOURCE MANAGEMENT

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1 GIS AND REMOTE SENSING FOR WATER RESOURCE MANAGEMENT G. GHIANNI, G. ADDEO, P. TANO CO.T.IR. Extension and experimental station for irrigation technique - Vasto (Ch) Italy. ghianni@cotir.it, addeo@cotir.it, tano@cotir.it ABSTRACT Through the use of Remote Sensing Techniques and G.I.S. it was produced a geo-referenced database regarding an area of irrigated land in Trigno valley (Southern Italy). It needs a better water irrigation management with respect to insufficient water availability. They have been realized two land use maps, one by photo interpretation and the other by high resolution remote sensing data (IRS-1C). The present paper shows the aims, the limits and the potentialities of the two employed methods. The first one allows to distinguish a great number of crop classes but it cannot be easily updated. The second one is a enough detailed map (1:10.000). It is cheap, easily updated, accurate and digital but it is composted only of few classes. Land information systems in order to evaluate water requirements for each crop were used for both the maps. Using G.I.S. it was localized the quantity of water needed in each field. INTRODUCTION In Italy, agricultural water management, maintenance, storage and distribution are managed by farmers union administrators. Regarding improvement in public sector productivity and service quality, some measures have to be adopted to minimize public intervention and expenditure (capital and recurrent) and to make more efficient use of water resources. Detailed and update maps are essential to plan activities and to assess natural resources. Resources can be better managed if information about crop, soil, climate, water, market etc. are in digital format so data can be more easily managed with a Geographic information system (GIS). Aerial photography still represents one of the best way to realize a detailed land use map. That method needs a big work of photo-interpretation and digitizing data. It requires time and professionality and it is quite expensive and not easy to update. In this last decads remote sensing data represent an alternative to aerial photos, in producing land use map. Even if it supplies good results at a scale not greater than 1: [Gomarasca 97]. Usually, compound Landsat and Spot imagery are arranged to improve detailes. In using satellite data there are some advantages: few ground control points are required, data are in digital format and georeferenced, data are easly to update. In fact remote sensing imagery, without sky cloudy limitation, are available in short interval (few days). Imagery are not cheap yet and land use application needs more than one image in multitemporal analysis. In the present study two land use maps were compared. The first derived by high resolution remote sensing data (IRS-1C image), the second one derived by aerial photos. Comparison regarded different aspects: land use accuracy, costs and updating and estimating water requirements for each crop. This research allows farmers union administrator of Trigno valley (Southern Italy) to have a land use map available and easy updated, in order to manage water resource and to schedule irrigation with G.I.S. support. 107

2 MATERIALS AND METHODS Investigated area The research concerns the Trigno valley, a plan irrigated land of 1670 hectares located in Abruzzo Region (FIG. 1). That area is managed by a farmers union administrator and it is characterized by high fragmented fields. The prevalent crops are peach trees, olive grove and vineyard. The present study is referred to the year FIGURE 1. The studied area: irrigation system and districts. Land use map To realize a detailed land use map by aerial photography, 1: photos coming from I.G.M.I. (Istituto Geografico Militare Italiano) were used. 38 crop classes following Corine Land Cover (European Community Commission, 1992) were distinguished. High resolution remote sensing IRS-1C data were used to obtain a enough detailed updated digital land use map. In order to minimize costs it was decided not to use a multitemporal analysis but only an image of coumpound subscenes dated 27 July The LISS and PAN image were processed, registrated and combined. A high resolution false color image was used to realized the land use map by Gaussian Maximum Likelihood (Swain and Davis, 1975) classification algorithm. The work was supported by some ground checks. The Kappa coefficient indicates classification accuracy percentage. Charta for Windows software was used to manage and classify the remote sensing data and Idrisi to vectorize the imagery made by Charta. Water requirements Water requirements for each crop was calculated multiplying crop evapotranspiration (ETcrop) by crop surface, for both land use maps. The ETcrop was calculated from meteorological and crop data. The evaporation power of the atmosphere is expressed by the reference crop evpotranspiration (ET 0 ). ET 0 represents the evapotranspiration from standardized vegetated surface, and it is calculated from meteorological data through the Hargreaves equation (Hargreaves, 1974). The meteorological data utilized are the daily average maximum and minimum air temperatures in Celsius degrees ( C). The data were 108

3 obtained from a meteorological station located in the studied area for the irrigated season of ETcrop was calculated multiplying ET 0 by the crop coefficient Kc (FAO, 1998). For each crop Kc changes during the growing period. The growing period can be divided into four distinct growth stages: initial (from planting date to approximately 10% ground cover), crop development (from 10% ground cover to effective full cover), mid-season (from effective full cover to the start of maturity) and late season (form the start of the maturity to harvest or full senescence). In the initial stage Kc is low, it increases at the crop development stage and it reaches the maximum value in the mid-season where it is relatively constant, then it decreases in the late season. The ETcrop is expressed in millimetres (mm) and the water requirement is expressed in m 3. G.I.S. In order to integrate the data collected, imagery, photos and to produce tematic maps we used different GIS software: the already mentioned Charta for Windows, Idrisi and also ArcView. ArcView was employed to integrate different layers, to calculate polygon areas, to add other attributes for each polygon, to determine water requirements and to produce thematic maps. RESULTS Land use map Land use map by aerial photography distinguishes a great number of crop classes (38 classes). The map is at plot scale but not in digital format. Further it can be updated only with very high costs (FIG. 2). FIGURE 2. Land use map by photo interpretation. The land use map (FIG. 3) by high resolution remote sensing data (IRS 1C) is an enough detailed map (1:10.000). It is easily updated and in digital format. The supervised classification applied to one LISS and PAN combined image has an accuracy greater than 86%. The land cover map is composed only of six classes. The vineyard and peach ones are not very well distinguished but the olive and the sowable classes are better discriminated than land use map from aerial photography. Anyway it is important to notice that the remote sensing evaluation of an artificial area is more accurate than using aerial photos. 109

4 FIGURE 3. Land use map by high resolution remote sensing data. It would be useful to evaluate the chance of elaborating more than one image (multi-temporal analysis) at higher costs to distinguish a greater number of crops. Actually it was possible to compare the costs of the two methodologies. The aerial photography is more expensive (about 9 %) than remote sensing data, TAB. 1. TABLE 1. Comparison costs in Materia l Mission s Workers Total Aerial photography , , ,364.2 Remote sensing 0 1, , Water requirements Monthly water requirements for each crop in the irrigated season of the area were calculated on the basis of the two land use maps obtained. Photo interpretation allows distinguishing a greater number of crop classes. It was reported only one graphic (with few classes) regarding the most extensive crops (FIG. 4). Water requirements (m 3 ) Monthly water requirements: most extensive crops (photo interpretation) Olive grove Vineyard Peach, plum and apricot trees Heterogeneous agricultural areas April M ay June July August September 110

5 FIGURE 4. Monthly water requirements by photo interpretation land use map: most extensive crop. Peach, with plum and apricot trees, are the crops that need more water, and June is the month in which there are the highest water requirements, in fact the year 1997 was the warmest month. Using land use map derived by remote sensing, it was calculated monthly water requirements for each crop in the irrigated season (FIG.5). Water requirements (m 3 ) Monthly water requirements (remote sensing) Other crops Sow able Olive grove Vineyard Peach 0 April M ay June July August September FIGURE 5. Monthly water requirements by remote sensing land use map. Also in this case peach is the crop which needs more water because it is the most extensive crop in this area. The other crops include some vegetables (tomato, artichoke, fennel and sugar beet) and some fruit trees. June is the month with the highest water requirements. In Figure 6 they were compared total monthly water requirements obtained from the two land use maps Water requirements comparison Remote sensing Aerial photography April M ay June July August September FIGURE 6. Monthly water requirements comparison for the two land use map. Although remote sensing and photo interpretation distinguish different crop classes, monthly water requirements are almost the same. GIS It was realized a georeferenced database. In this database each field is represented by a polygon and each polygon has different attributes: area, land use, Corine land cover index, yearly evapotranspiration (mm) and field yearly water requirements (m 3 ) (FIG. 7). 111

6 FIGURE 7. Polygonal attributs. CONCLUSION In Italy high resolution remote sensing data can change the world of detailed scale land use mapping, nowadays that relies mainly on aerial photography (Volpe, 1997). This study shows that if the high resolution remote sensing data will be cheaper, they will came a valid alternative to aerial photography in order to realize detailed land use map, especially in zone, like Italy, where fields size are small (few hectares). Actually to produce a detailed map and to discriminate better crop classes it is necessary a multitemporal analysis, so it occurs more than one image. Moreover it was showed that with an updated digital detailed land use map and meteorological data it is possible to evaluate water requirements for each crop. The analysis of land cover by high-resolution remote sensing data and the elaboration of the available data (climatic data, water requirement etc.) allowed to produce a Support Decision System using GIS software. Georeferenced database and maps allow to manage water in response to water crop requirement to each crop and district. In this way water reservoir can be used to irrigate stressed crop or crop with higher economic value. Finally farmers union administrator can easily manage irrigation resources to plan water resources, to reduce environmental impact due to agriculture and to manage other natural resources successfully. REFERENCES European Community Commission (1992) Corine Land Cover, Brochure made for European Conference of International Space Year, Monaco 30 March- 4 April FAO (1998) Crop evapotranspiration guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper 56, Rome. Hargreaves G.H. (1974) Estimation of potential and crop evapotranspiration, Trans. ASAE 17. Swain P.H., Davis S.M. (1978) Remote sensing: the quatitative approach, McGraw-Hill. Gomarasca M. (1997) Introduzione a telerilevamento e GIS per la gestione delle risorse argicole e ambientali, AIT. Volpe F., Dati da satellite ad alta risoluzione:l alternativa futura alle foto aeree, ASITA 1997 Conference Act. 112

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